Embodiments of the present specification relate generally to automatic detection and characterization of anatomical, physiological and pathological features using medical imaging modalities and more particularly to, systems and methods for detecting and characterizing anatomical, physiological and pathological features by processing scanned data by deep learning technique.
Various imaging systems (also referred to as modalities) such as CT (computed tomography), MRI (magnetic resonance imaging), X-ray systems, US (ultrasound), and PET (positron emission tomography) are used for acquiring image datasets and generating images of anatomical structures of individuals for screening and evaluating medical conditions. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions, functional or anatomical abnormalities, including, for example, cardiomyopathy, colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, cancer micro calcifications or masses in breast tissue, and various other lesions or abnormalities. Some of the imaging techniques are also useful in other applications such as non-destructive testing in industrial applications.
Manual review and evaluation of medical images is employed by medical professionals to identify region of interest in the medical image and diagnose potential medical conditions. In general, medical images may be processed to automatically identify the anatomy and automatically assess the diagnostic quality of the medical image. Automated decision support for medical imaging includes extracting feature data from the image data, and automatically performing anatomy identification, view identification and/or determining a diagnostic quality of the image data, using the extracted feature data. Similarly, CT image datasets acquired during inspection from an aircraft engine may be used for extracting feature data representative of structural defects of the engine.
Emerging machine learning techniques such as deep learning networks are increasingly being used from the image datasets to extract anatomical features that are useful in examining the anatomy under consideration and diagnosing the underlying medical conditions of the subject.